The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several appl...

The benefits of Hyperion hyperspectral data to agriculture have been studied at sites in the Coleambally Irrigation Area of Australia. Hyperion can provide effective measures of agricultural performance through the use of established spectral indexes if systematic and random noise is managed. The noise management strategy includes recognition of “bad” pixels, reducing the effects of vertical st...

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sen...

This paper describes a new algorithm for feature extraction on hyperspectral images based on blind source separation (BSS) and distributed processing. I use Independent Component Analysis (ICA), a particular case of BSS, where, given a linear mixture of statistical independent sources, the goal is to recover these components by producing the unmixing matrix. In the multispectral/hyperspectral i...

The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD. This paper presents an approach to the progressive line processing of KRXD (PLP-KRXD) that can perform KRXD line by line (the main data acquisition pattern). P...

Based on hyperspectral data mining techniques and characteristics of imaging spectrum, Hyperion satellite hyperspectral data are used to study the diagnosable spectral signatures and quantitatively extract the absorption spectral features of sandstone-hosted uranium deposits in northern China. In terms of information extraction approach and the processing method of extra-abundance data, we have...

Hyperspectral remote sensing images provide richer information about materials than that of multispectral images. The new larger data volumes of hyperspectral sensors bring new challenges for traditional image processing techniques. Therefore, conventional classification methods could fail without employing dimension reduction preprocessing. The dimensional reduction methods can be totally divi...

Hyperspectral imaging systems, used in conjunction with appropriate detection and recognition algorithms, have demonstrated to be very appropriate tools for standoff detection in many different environments. Compared to other techniques available such as multispectral imaging, which typically collects only tens of images, hyperspectral instruments are capable of collecting hundreds of images, c...

Hyperspectral imaging has been widely studied in many applications; notably in climate changes, vegetation, and desert studies. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and spaceborne imaging. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compressio...

Hyperspectral image processing algorithms are computationally very costly, which makes them good candidates for parallel and, specifically, GPU processing. Extreme Learning Machine (ELM) is a recently proposed classification algorithm very suitable for its implementation on GPU platforms. In this paper we propose an efficient GPU implementation of an ELM-based classification strategy for hypers...